package com.atguigu.gmall.realtime.app.dwm;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.atguigu.gmall.realtime.utils.MyKafkaUtil;
import org.apache.flink.api.common.functions.RichFilterFunction;
import org.apache.flink.api.common.state.StateTtlConfig;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;

import java.text.SimpleDateFormat;

/**
 * Author: Felix
 * Date: 2022/4/23
 * Desc: 独立访客计算
 * 需要启动的进程
 *      zk、kafka、logger（Nginx + 日志采集服务）、BaseLogApp、UniqueVisitorApp
 */
public class UniqueVisitorApp {
    public static void main(String[] args) throws Exception {
        //TODO 1.基本环境准备
        //1.1 指定流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //1.2 设置并行度
        env.setParallelism(4);
        //TODO 2.检查点相关的设置(略)
        //TODO 3.从kafka中读取数据
        //3.1 声明消费的主题以及消费者组
        String topic = "dwd_page_log";
        String groupId = "unique_visitor_group";
        //3.2 创建消费者对象
        FlinkKafkaConsumer<String> kafkaSource = MyKafkaUtil.getKafkaSource(topic, groupId);
        //3.3 消费数据 封装为流
        DataStreamSource<String> kafkaStrDS = env.addSource(kafkaSource);

        //TODO 4.对读取的数据类型进行转换       jsonStr->jsonObj
        SingleOutputStreamOperator<JSONObject> jsonObjDS = kafkaStrDS.map(JSON::parseObject);

        //jsonObjDS.print(">>>>>");

        //TODO 5.按照mid对数据进行分组
        KeyedStream<JSONObject, String> keyedDS = jsonObjDS.keyBy(jsonObj -> jsonObj.getJSONObject("common").getString("mid"));

        //TODO 6.使用Flink的状态编程，过滤出一天的UV
        SingleOutputStreamOperator<JSONObject> filterDS = keyedDS.filter(
            new RichFilterFunction<JSONObject>() {
                private ValueState<String> lastVisitDateState;
                private SimpleDateFormat sdf;

                @Override
                public void open(Configuration parameters) throws Exception {
                    sdf = new SimpleDateFormat("yyyyMMdd");
                    ValueStateDescriptor<String> valueStateDescriptor = new ValueStateDescriptor<>("lastVisitDateState", String.class);
                    valueStateDescriptor.enableTimeToLive(
                        StateTtlConfig.newBuilder(Time.days(1))
                            //.setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)
                            //.setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)
                            .build()
                    );
                    lastVisitDateState = getRuntimeContext().getState(valueStateDescriptor);
                }

                @Override
                public boolean filter(JSONObject jsonObj) throws Exception {
                    //如果当前页面  是从其它页面跳转过来的，说明这次访问就不属于独立访客了，直接过滤掉
                    String lastPageId = jsonObj.getJSONObject("page").getString("last_page_id");
                    if(lastPageId != null && lastPageId.length() > 0){
                        return false;
                    }

                    //判断状态中是否存在访问记录
                    String lastVisitDate = lastVisitDateState.value();
                    String curVisitDate = sdf.format(jsonObj.getLong("ts"));
                    if(lastVisitDate != null && lastVisitDate.length() > 0 && lastVisitDate.equals(curVisitDate)){
                        //说明当前设备 在今天已经访问过了
                        return false;
                    }else{
                        //说明当前设备 在今天还访问没有访问过
                        lastVisitDateState.update(curVisitDate);
                        return true;
                    }
                }
            }
        );

        filterDS.print(">>>>");
        //TODO 7.将过滤的结果输出到kafka的dwm_unique_visitor
        filterDS
            .map(jsonObj->jsonObj.toJSONString())
            .addSink( MyKafkaUtil.getKafkaSink("dwm_unique_visitor"));
        env.execute();
    }
}
